fNIRS Approach to Pain Assessment for Non-verbal Patients

Raul Fernandez Rojas, Xu Huang, Julio Romero, Keng Liang Ou

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)

Abstract

The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (88.33 %) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy
Place of PublicationCham, Switzerland
PublisherSpringer
Pages778-787
Number of pages10
ISBN (Electronic)9783319700939
ISBN (Print)9783319700922
DOIs
Publication statusPublished - Nov 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10637 LNCS

Conference

Conference24th International Conference on Neural Information Processing, ICONIP 2017
CountryChina
CityGuangzhou
Period14/11/1718/11/17

Fingerprint

Near-infrared Spectroscopy
Near infrared spectroscopy
Pain
Biomarkers
Nearest Neighbor
Wavelets
Learning systems
Time series
Dementia
Codebook
Cross-validation
Histogram
Communication
Computational Cost
Time Domain
Machine Learning
High Accuracy
Heat
Hot Temperature
Costs

Cite this

Rojas, R. F., Huang, X., Romero, J., & Ou, K. L. (2017). fNIRS Approach to Pain Assessment for Non-verbal Patients. In D. Liu, S. Xie, Y. Li, D. Zhao, & E-S. M. El-Alfy (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 778-787). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10637 LNCS). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-70093-9_83
Rojas, Raul Fernandez ; Huang, Xu ; Romero, Julio ; Ou, Keng Liang. / fNIRS Approach to Pain Assessment for Non-verbal Patients. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Derong Liu ; Shengli Xie ; Yuanqing Li ; Dongbin Zhao ; El-Sayed M. El-Alfy. Cham, Switzerland : Springer, 2017. pp. 778-787 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "fNIRS Approach to Pain Assessment for Non-verbal Patients",
abstract = "The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (88.33 {\%}) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.",
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Rojas, RF, Huang, X, Romero, J & Ou, KL 2017, fNIRS Approach to Pain Assessment for Non-verbal Patients. in D Liu, S Xie, Y Li, D Zhao & E-SM El-Alfy (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10637 LNCS, Springer, Cham, Switzerland, pp. 778-787, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14/11/17. https://doi.org/10.1007/978-3-319-70093-9_83

fNIRS Approach to Pain Assessment for Non-verbal Patients. / Rojas, Raul Fernandez; Huang, Xu; Romero, Julio; Ou, Keng Liang.

Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Derong Liu; Shengli Xie; Yuanqing Li; Dongbin Zhao; El-Sayed M. El-Alfy. Cham, Switzerland : Springer, 2017. p. 778-787 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10637 LNCS).

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - fNIRS Approach to Pain Assessment for Non-verbal Patients

AU - Rojas, Raul Fernandez

AU - Huang, Xu

AU - Romero, Julio

AU - Ou, Keng Liang

PY - 2017/11

Y1 - 2017/11

N2 - The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (88.33 %) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.

AB - The absence of verbal communication in some patients (e.g., critically ill, suffering from advanced dementia) difficults their pain assessment due to the impossibility to self-report pain. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that has showed promising results in assessing cortical activity in response to painful stimulation. In this study, we used fNIRS signals to predict the state of pain in humans using machine learning methods. Eighteen healthy subjects were stimulated using thermal stimuli with a thermode, while their cortical activity was recorded using fNIRS. Bag-of-words (BoW) model was used to represent each fNIRS time series. The effect of different step sizes, window lengths, and codebook sizes was investigated to improve computational cost and generalization. In addition, we explored the effect of choosing different features as neurological biomarkers in three different domains: time, frequency, and time-frequency (wavelet). Classification on the histogram representation was performed using K-nearest neighbours (K-NN). The performance is evaluated by using leave-one-out cross validation and with different nearest neighbours. The results showed that wavelet-based features produced the highest accuracy (88.33 %) to distinguish between heat and cold pain while discriminate between low and high pain. It is possible to use fNIRS to assess pain in response to four types of thermal pain. However, future research is needed for the assessment of pain in clinical settings.

KW - Brain

KW - Haemodynamic

KW - Multiclass

KW - Neural

KW - Pain

KW - Time series

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UR - http://www.mendeley.com/research/fnirs-approach-pain-assessment-nonverbal-patients

U2 - 10.1007/978-3-319-70093-9_83

DO - 10.1007/978-3-319-70093-9_83

M3 - Conference contribution

SN - 9783319700922

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 778

EP - 787

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Liu, Derong

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A2 - El-Alfy, El-Sayed M.

PB - Springer

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Rojas RF, Huang X, Romero J, Ou KL. fNIRS Approach to Pain Assessment for Non-verbal Patients. In Liu D, Xie S, Li Y, Zhao D, El-Alfy E-SM, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Cham, Switzerland: Springer. 2017. p. 778-787. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-70093-9_83